International Journal of Food Sciences and Nutrition
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Validation of a food frequency questionnaire for use in Italian adults living in Sicily Silvio Buscemi, Giuseppe Rosafio, Sonya Vasto, Fatima Maria Massenti, Giuseppe Grosso, Fabio Galvano, Nadia Rini, Anna Maria Barile, Vincenza Maniaci, Loretta Cosentino & Salvatore Verga To cite this article: Silvio Buscemi, Giuseppe Rosafio, Sonya Vasto, Fatima Maria Massenti, Giuseppe Grosso, Fabio Galvano, Nadia Rini, Anna Maria Barile, Vincenza Maniaci, Loretta Cosentino & Salvatore Verga (2015) Validation of a food frequency questionnaire for use in Italian adults living in Sicily, International Journal of Food Sciences and Nutrition, 66:4, 426-438 To link to this article: http://dx.doi.org/10.3109/09637486.2015.1025718
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Date: 11 September 2015, At: 15:04
http://informahealthcare.com/ijf ISSN: 0963-7486 (print), 1465-3478 (electronic) Int J Food Sci Nutr, 2015; 66(4): 426–438 ! 2015 Informa UK Ltd. DOI: 10.3109/09637486.2015.1025718
STUDIES IN HUMANS
Validation of a food frequency questionnaire for use in Italian adults living in Sicily Silvio Buscemi1, Giuseppe Rosafio1, Sonya Vasto2, Fatima Maria Massenti3, Giuseppe Grosso4, Fabio Galvano4, Nadia Rini1, Anna Maria Barile1, Vincenza Maniaci1, Loretta Cosentino1, and Salvatore Verga1 Downloaded by [University of Nebraska, Lincoln] at 15:04 11 September 2015
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Dipartimento Biomedico di Medicina Interna e Specialistica (DIBIMIS) – Laboratorio di Nutrizione Clinica, 2Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche (DIBIMEF) and 3Dipartimento di Scienze per la Promozione della Salute e Materno Infantile, University of Palermo, Palermo, Italy, and 4Dipartimento di Scienze del Farmaco, University of Catania, Catania, Italy
Abstract
Keywords
The objective of this study was to validate two interviewer-led food frequency questionnaires (FFQs) of very different lengths: a medium-length FFQ (medium-FFQ) of 36 items and a shortlength FFQ (short-FFQ) of 18 items, intending to measure levels of intakes in a local population. Both FFQs were validated against intakes derived from a 3-day dietary record (3-day DR). Sixtyfive non-diabetic adults with no known cardiovascular, renal or other systemic diseases were included. High correlation coefficients between the FFQ and the 3-day DR (0.45–0.73) were observed for energy intake, carbohydrates and lipid and protein intake. Bland–Altman plots showed good agreement between the methods. Low (0.26–0.37) correlation coefficients of the different nutrient intakes obtained with the short-FFQ and the 3-day DR were observed, with the exception of alcohol intake (rho ¼ 0.49). This study showed promising evidence for the use of a medium-FFQ as a potentially useful tool for investigating the relationship between habitual diet and diseases in clinical and research settings.
Dietary record, energy intake, food frequency questionnaire, food intake
Introduction Dietary habits are important determinants of lifestyle, and play a central role in the pathogenesis and treatment of a number of clinical conditions, particularly metabolic diseases such as diabetes (Knowler et al., 2002) and atherosclerosis (Stampfer et al., 2000). It is therefore particularly important that there be adequate tools for investigating the role of dietary habits, and for developing both prevention and intervention strategies in the special context of metabolic and cardiovascular diseases. Even on clinical grounds, investigating dietary habits is a very useful step toward targeting patients affected with diseases such as obesity, diabetes or atherosclerosis (Franz et al., 2004). No dietary method can measure dietary intake without error (Margetts & Nelson, 1997), so limits and the possibility of error need to be defined in order to avoid undue conclusions. Food frequency questionnaires (FFQs) are low-cost and easy-to-use tools (Block et al., 1990; Jacques et al., 1993). Traditionally, they have been used largely to describe habitual dietary intake, particularly in epidemiologic studies. Food consumption is culture dependent (Cassidy, 1994; Vasto et al., 2014), and therefore FFQs need to be adequately validated for the population of interest, as well as the degree to which the questionnaire measures items (foods or nutrients) for which it has been designed. Incorrect information may lead to false associations between dietary factors
History Received 27 October 2014 Revised 4 February 2015 Accepted 15 February 2015 Published online 1 April 2015
and diseases or disease-related markers. The Italian population was part of the Seven Country Study, which contributed significantly to describing the Mediterranean diet (Keys et al., 1986). However, dietary habits have changed profoundly (Sofi et al., 2005) since the time of that study, and developing current Italian regional FFQs may provide important information about the evolution of dietary habits in the last few decades. Various FFQs have been proposed, with substantial differences in terms of length (from 5 to 350 items) and consequent time required for their use (Cade et al., 2004). The length of a FFQ is critical in ensuring an appropriate balance between accuracy and time of use, thus allowing valid application in large samples of a population. Relatively few studies have been conducted to evaluate the validity of FFQs among Italians, and no study of residents of Sicily (one of the largest regions in Italy), where there is a high prevalence of diet-related diseases, such as obesity and diabetes (Buscemi et al., 2013). Therefore, we designed two interviewer-led FFQs of very different lengths: a medium-length (36 items) FFQ (medium-FFQ) and a short (18 items) FFQ (shortFFQ), intending to measure levels of intakes in the local population. The aim of this study was to validate both FFQs versus a 3-day dietary record (3-day DR), one of the most widely used validation methods in the literature (Cade et al., 2004).
Materials and methods Selection of participants Correspondence: Silvio Buscemi, Dipartimento Biomedico di Medicina Interna e Specialistica (DIBIMIS) – Laboratorio di Nutrizione Clinica, University of Palermo, Palermo, Italy. Tel/Fax: +39 0916554580. E-mail: [email protected]
Participants were recruited from the cohort drawn from the general population of a previous study held in Palermo that investigated the relationship between diet and cardiometabolic
Food frequency questionnaire in Italian adults
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DOI: 10.3109/09637486.2015.1025718
risk factors (Buscemi et al., 2013). Briefly, participants were invited to participate in the study through telephone contact or e-mail. People of both genders with age ranging from 18 to 80 years were included. Exclusion criteria were the presence of diabetes (type 1 or 2) on insulin treatment, clinically known atherosclerotic diseases (coronary heart disease, previous stroke, carotid or peripheral atherosclerosis), chronic renal failure and other systemic or organ failure diseases including neurodegenerative disorders, as determined by medical history, physical examination and routine blood tests. The study was conducted from January to December 2013. Participants were asked to answer a medium-FFQ designed according to local dietary habits. They were also requested to compile a 3-day DR within the following 2 weeks, during which they were also asked to maintain their usual lifestyle. When participants returned to our center with the compiled 3-day DR, a short-FFQ was also administered. Energy intake and macro- and micro-nutrients were calculated using the Metadieta software application (Me.Te.Da., San Benedetto del Tronto, Italy), which includes the Italian database of food composition (INRAN/IEO, 2008). Data were entered twice by two registered dieticians and then analyzed and carefully checked for errors by a third registered dietician. The dieticians were blinded to the study. The final report indicated the reported intakes of calories, fats (saturated, monounsaturated, unsaturated), carbohydrates, proteins, fiber and micro-nutrients. Data were collected quarterly, beginning in January 2013, using all three tools within the same month to minimize seasonal variations in eating patterns. At first visit, demographic and clinical information was collected. Height (m) and body weight (kg) were measured by a professional nurse; body mass index was calculated as body weight/height2 (kg/m2). The study was conducted according to the guidelines in the Declaration of Helsinki, and the study protocol was approved by the Investigator Revisory Board at the Biomedical Department of Internal and Specialist Medicine of the University of Palermo. Each participant approved and signed an informed consent form. Three-day dietary record Habitual energy intake was assessed using the 3-day DR method, as previously described (Buscemi et al., 1997). Briefly, it consisted of a three-page standardized recording form with written instructions: participants were instructed to record all food and beverages they had consumed for 3 consecutive days, including a Sunday, in a food diary. The 3-day DR included photographs of 15 foods, each with three different portion sizes. Participants could choose which photograph represented their portion size, and were also allowed to describe their portion size in other measures, such as weight or household units. The 3-day DR was used as the reference method. FFQ and short-FFQ Half-quantitative habitual intake of different foods during the previous 12 months was assessed with a newly designed mediumFFQ as presented in Appendix A. Specifically, participants were asked by trained dieticians about their consumption of 36 different food items based on commonly eaten foods and portion sizes of the food list relative to previous data collected through a 24-h dietary recall from 422 residents in the urban area of Palermo (Buscemi et al., 2011). Participants were also asked to indicate their usual rate of consumption, choosing from seven frequency categories, ranging from ‘‘never’’ or ‘‘less than once a week’’ to ‘‘seven times per week.’’ The food items were categorized as drinks, milk and dairy products, meat–fish–eggs, cereals, vegetables–legumes–fruit, fatty dressings and other (sweets, fried foods and fast food). For coffee, alcoholic and soft drinks, the
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amount in terms of cups/glasses was also specified. In most instances, a reference food was considered for different items, and photographs of food were used to help in portion-size description (Atlas Scotti Bassani, 2010), as indicated in Appendix B. Requested data referred to the last year. A short version of the above-described FFQ (short-FFQ) was also designed. It was interviewer-led and consisted of 18 items, as presented in Appendix C. Statistical analysis Participants with a missing or incomplete 3-day DR were excluded from evaluation. Participants with evidence of clear underreporting of food intake from the 3-day DR (their reported energy intake was lower than the predicted basal metabolic rate in the presence of a stable body weight) were also excluded. Since a different body weight between the two test days may indicate different energy intakes at the time of each test (FFQs versus 3-day DR), participants whose body weight from medium-FFQ measurement to the 3-day DR changed 4± 1.0 kg were also excluded. Normal distribution of the variables was tested using the Kolmogorov–Smirnov test. The mean and standard deviation for each nutrient obtained from the medium-FFQ, short-FFQ and 3-day DR were calculated. The validity of both the medium-FFQ and the short-FFQ was compared with the 3-day DR using Pearson correlation coefficients. Rank correlation with Spearman’s coefficient (rho) was calculated in the case of not normally distributed variables with observations of zero values. Between-method differences were analyzed with the Wilcoxon test. A p value of less than 0.05 was considered statistically significant. The Bland–Altman method was used to produce quantitative estimates of agreement between the methods (Bland & Altman, 1986). The Bland–Altman analysis was performed for energy, protein, fat and carbohydrate intake. The sample size of the analysis is within the range of previous studies in this field (Cade et al., 2002). All statistical analyses were performed using MedCalc Statistical Software version 13.3.3 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014).
Results One-hundred-eighteen volunteers gave their consent to participate in the investigation; 87 were eligible, and 22 were excluded due to inadequate 3-day DR based on previously established criteria. Therefore, 65 participants were considered for analysis. Demographic and clinical characteristics of participants are reported in Table 1. The nutritional variables were normally distributed except for alcohol, fiber, cholesterol, polyunsaturated fat and n3 and n6 fatty acid intakes obtained with all three methods used in the study. Correlation coefficients for the variables obtained using the FFQ and 3-day DR are reported in Table 1. Significant correlations were observed for all dietary variables with the exception of cholesterol, calciferol and potassium. Moderate to high correlation coefficients (0.45–0.73) with good agreement between the two methods were observed for energy intake (kcal/ day), carbohydrates (g/day), and lipid (g/day) and protein intake (g/day) (Figure 1). However, the medium-FFQ underestimated energy intake by an average of 10.6%, and carbohydrate intake by 35%, with minimal bias for protein and fat intakes. Correlation coefficients higher than 0.40 (0.48–0.65) were also observed for saturated, monounsaturated, polyunsaturated, n6 fat, calcium and phosphorus intakes, as well as for alcohol intake (rho ¼ 0.39). However, coupled with examination of the Bland–Altman plots (Figure 2), a lack of agreement between methods was observed for polyunsaturated fatty acid and n6 fatty acid intakes (Figure 2C and D). The Bland–Altman plots confirmed a good
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Table 1. Participant characteristics, daily energy and nutrient intakes according to 3-Day DR, and medium and short FFQ.
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Mean ± SD or prevalence Males/females, n (%) Age (years) Marital status Married, n (%) Single, n (%) Smoking habits Current smokers, n (%) Cigarette/day (n) Ex-smokers, n (%) Coffee consumption (cups/day) Body weight (kg) BMI (kg/m2) Waist circumference (cm) Waist-to-hip ratio Systolic BP (mmHg) Diastolic BP (mmHg) Heart rate (beats/min)
Energy intake (kcal/day) Carbohydrate (g/day) Protein (g/day) Fat (g/day) Saturated fat (g/day) Monounsaturated fatty acids (g/day) Polyunsaturated fatty acids (g/day)b n3 Fatty acids (g/day)b n6 Fatty acids (g/day)b Cholesterol (mg/day)b Alcohol (g/day)b Fiber (g/day)b Calciferol (mcg/day) Calcium (mg/day) Phosphorus (mg/day) Sodium (mg/day) Potassium (mg/day)
26 (40)/39 (60) 53 ± 15
Range 18–79
58 (89.2) 7 (10.8) 8 (12.3) 19 ± 10 23 (35.4) 2.5 ± 1.6 93.7 ± 25.1 35.0 ± 7.3 110 ± 17 0.96 ± 0.09 139 ± 18 85 ± 12 72 ± 10
0–6 44–164 21.8–60.3 72–156 0.73–1.09 100–185 65–115 50–91
3-Day DR
Medium-FFQ
1825 ± 464 234 ± 60 73 ± 20 70 ± 27 22 ± 10 37 ± 11 8±4 0.8 ± 0.4 6.2 ± 3.8 237 ± 117 2.6 ± 11.2 19 ± 6 2.6 ± 2.7 659 ± 258 1081 ± 275 1689 ± 832 2404 ± 638
(1044–3331) (116–441) (44–122) (15–175) (5–60) (10–77) (3–26) (0.3–2.5) (2.0–21.7) (52–573) (0–86) (10–40) (0.2–13.6) (218–1620) (486–1651) (116–4548) (1059–4382)
5–40
1618 ± 372 155 ± 44 72 ± 15 77 ± 26 24 ± 7 38 ± 15 8±4 0.9 ± 0.3 6.6 ± 3.3 280 ± 68 4 ± 10 13 ± 4 1.5 ± 0.5 766 ± 208 1132 ± 216 1057 ± 302 2247 ± 455
(733–2441) (72–330) (45–119) (29–160) (11–42) (13–93) (3–21) (0.4–1.6) (1.9–19) (115–480) (0–64) (7–28) (0.6–2.7) (347–1243) (691–1554) (452–1717) (1005–3300)
ra
p
0.73 0.56 0.45 0.68 0.48 0.65 0.48 0.31 0.49 0.23 0.93 0.32 0.11 0.48 0.54 0.29 0.18
50.001 50.001 50.001 50.001 50.001 50.001 50.001 0.01 50.001 0.07 50.001 0.01 0.46 50.001 50.001 0.02 0.17
Short-FFQ 959 ± 178 61 ± 16 51 ± 9 54 ± 10 18 ± 3 32 ± 5 4±1 0.5 ± 0.1 3.6 ± 0.6 183 ± 33 4 ± 10 5±1 1.0 ± 0.3 633 ± 154 805 ± 141 686 ± 172 1240 ± 289
(645–1636) (19–82) (32–67) (23–71) (7–23) (10–35) (0.4–0.7) (0.3–0.7) (1.8–4.7) (115–251) (0–64) (2–6) (0.5–1.9) (330–884) (512–1123) (342–1040) (580–2486)
ra
p
0.32 0.01 0.20 0.11 0.32 0.01 0.37 0.003 0.26 0.04 0.33 0.009 0.19 0.15 0.12 0.36 0.17 0.18 0.19 0.14 0.92 50.001 0.24 0.06 0.21 0.11 0.30 0.018 0.36 0.004 0.10 0.44 0.02 0.90
Values are means ± SD, range in parenthesis. a Pearson’s correlation coefficient (Spearman’s coefficient of rank correlation in the case of alcohol). b Not normally distributed variables, log-transformed for analysis and then back-transformed for presentation. BMI, body mass index; BP, blood pressure.
between-methods agreement for saturated and monounsaturated fat intakes (Figure 2A and B), calcium and phosphorus intakes (Figure 3) and alcohol consumption (Figure 4A). Low but significant r values (0.29–0.37) were found with regard to n3 fatty acids, fiber and sodium intakes. As a result, Bland–Altman analysis was not performed. Energy and seasonal adjustment did not improve the observed between-methods correlations for the above nutrients (data not shown). The correlation coefficients of the different nutrient intakes obtained with the short-FFQ and 3-day DR are reported in Table 1. Significant correlations were observed for energy intake and for the daily intakes of proteins, fats, saturated fats, monounsaturated fatty acids (MUFA), alcohol, calcium and phosphorus. However, the correlation coefficients were rather low (0.26–0.37) with the exception of alcohol intake (rho ¼ 0.49). The short-FFQ highly underestimated energy (47.5% on average), fat (19.4%) and protein intakes (30.1%). Given the low values of the correlation coefficients, the Bland–Altman analysis was not performed, with the exception of alcohol intake (Figure 4B), which confirmed a good between-methods agreement.
Discussion It is imperative that a FFQ be designed and validated for the population in whom it is used. Differences between populations
make it necessary to develop culturally sensitive tools for the evaluation of dietary habits. In this study, we attempted to validate two FFQs (a medium-length and a short-length FFQs) designed to assess current diet in Italian adults living in Sicily, a Mediterranean-diet-based population. Both FFQs were compared with a 3-day DR. The results confirmed the hypothesis that the medium-FFQ provides evaluations similar to the 3-day DR for many of the variables investigated. By contrast, the hypothesis was not true with regard to the evaluations obtained with the short-FFQ, as considerably large differences with values obtained with 3-day DR were observed. This was not the case for habitual alcohol consumption, thus confirming that short-length FFQs may be applicable only when single nutrient intakes have to be assessed (Cade et al., 2004; Montomoli et al., 2002). Our medium-FFQ provided a good evaluation of different variables, including energy intake and the intakes of lipids, proteins, carbohydrates, SFA, MUFA, calcium, phosphorus and alcohol. The agreement between the medium-FFQ and the 3-day DR was verified according to the recommendations of the consensus document on the development of a medium-FFQ (Cade et al., 2002), using correlation analysis in conjunction with the Bland– Altman method. Even with the medium-FFQ significant differences with respect to the 3-day DR estimates were observed. The medium-FFQ underestimated the energy and carbohydrate
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Figure 1. Bland–Altman plots for energy intake and macronutrients (A, energy; B, carbohydrate; C, fat; D, protein intakes) with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
intakes. Over/under estimation by FFQs is an expected error, and similarly to other studies (Jackson et al., 2011; Xia et al., 2011) we did not observe improvements in any correlation after energy adjustments (data not shown). It is worth considering that as in our study, validation studies of FFQs are generally (Cade et al., 2002) performed in comparison with dietary record methods that are far from being real gold standards. In this case, a gold standard in terms of ‘‘truth reference’’ is not available, and food records suffer from measurement errors that are likely correlated with errors in the medium-FFQ (Goris et al., 2000; Ocke & Kaaks, 1997). Given the absence of a real gold standard it would be more appropriate to refer to studies of relative validity than to validation studies. Interestingly, we also found that mean calcium intake estimated according to the 3-day DR (659 mg/day) or the medium-FFQ (766 mg/day) was almost similar to the mean value (820 mg) of the Italian population as reported by a wider research study (SINU, 1996). This difference could likely be rectified by estimating the intake of calcium in water (Montomoli et al., 2002), a variable that was not considered in our study. Furthermore, a more recent Italian survey (Sette et al., 2011) indicated an average habitual calcium intake of 767 mg/day, a value that is more similar to the estimation we obtained. In their review, Cade et al. (2004) observed that the method of administration of an FFQ played a critical role in validation studies, and correlation coefficients between the FFQ and reference measures were generally higher for interviewer-administered than for self-administered questionnaires. The good level of accuracy found in our medium-FFQ, despite its limited number of items, may be due, at least in part, to the fact that it was
administered by registered dieticians and not self-administered. Furthermore, our medium-FFQ was newly designed, and in the same review by Cade et al. it was reported that correlation coefficients were higher for newly designed FFQ than for those adapted from other questionnaires. With the exception of the FFQ designed for the European Prospective Investigation into Cancer and Nutrition (EPIC) study (Decarli et al., 1996; Faggiano et al., 1992; Pala et al., 2003; Pisani et al., 1997), the aim of which was to investigate the associations between diet and cancer, no FFQ has been designed to assess the current diet of people living in South Italy. Italian FFQs have been developed to assess the habitual intake of specific micro-nutrients (Cena et al., 2008; Gonnelli et al., 2009; Montomoli et al., 2002; Pellegrini et al., 2007; Porrini et al., 1995; Turconi et al., 2010), the diet risk associated with cancers (Cade et al., 2004), disease-specific diet associations (Cade et al., 2004), current diets in northern Italy (Fidanza et al., 1995) and, finally, in children/adolescents (Bellu et al., 1996; Bertoli et al., 2005; Pampaloni et al., 2013). The medium-FFQ that we validated might be a useful tool for investigating the associations between diet, metabolic and cardiovascular diseases in this population at high risk for obesity and diabetes. A number of FFQs have been proposed, with the number of items ranging from 5 to 350, thus varying from very easy, but imprecise, questionnaires (as was our short-FFQ) to very accurate questionnaires, which are, however, time-consuming and stressful for the participant (Cade et al., 2004). Very detailed and long FFQs may lead to potential biases when administered to people
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Figure 2. Bland–Altman plots for dietary contents of (A) saturated fatty acids (SFA); (B) monounsaturated fatty acids (MUFA); (C) polyunsaturated fatty acids (PUFA) and (D) n6 fatty acids (n6 FA) with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
Figure 3. Bland–Altman plots for dietary contents of (A) calcium and (B) phosphorus with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
with low levels of education, especially when self-administered. Our medium-FFQ takes about 10–15 min to be administered, and may have the merit of conjugating optimal length and accuracy. Our study has several limitations, one of which is the rather limited number of participants, although the number of participants in our study was in the recommended range (50–100) for
this kind of study (Cade et al., 2002), and it has been noted (Cade et al., 2004) that the size of the validation study does not make an appreciable difference to the study results. The main limitation of our study may be that we did not specifically address the issue of repeatability, however, it has been affirmed (Altman, 1995) that a method with poor repeatability hardly agrees well
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Figure 4. Bland–Altman plots for alcohol intake with mean difference (solid line) between medium-length (A), short-length (B) FFQ and 3-day DR and 95% limits of agreements (dashed lines).
with another method, and that good agreement is most unlikely unless the two methods are both accurate and repeatable. Although we did not measure the interviewer inter- and intravariability, another limitation, the dieticians were adequately trained, and we believe that in the case of significant interviewer inter- and intra-variability our results would have been weaker. In conclusion, our study shows that a medium-FFQ is not difficult to design for specific populations, and likely improves the accuracy of studies that aim to investigate the relationship between habitual diet and diseases.
Delcaration of interest This study was funded by Associazione Onlus Nutrizione e Salute, Palermo, Italy.
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Appendix A. Medium-length FFQs
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Appendix B. Procedure used to calculate energy and nutrients content from FFQ
Categories
Items
Drinks
Coffee Wine Beer Strong drinks Soft drinks Milk Yogurt Hard cheese Soft cheese Mozzarella Cottage cheese Red meat Poultry Pork Fish Cured meat Salami Eggs Pasta/rice Bread Bakery products
Milk and dairy products
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Meat, fish and eggs
Cereals
Vegetables, legumes and fruits
Fatty dressings
Other
Potatoes Pizza Vegetables/greens Dried legumes Fresh legumes Fresh Fruits Olive oil Seed oil Butter Margarine Sweets Fried foods Fast food Hamburger Hot dog Local street food
Scotti-Bassani Atlas reference (table number)
MetaDieta software selection
Cup (n.99) Glasses 2 (n.94) Bottles and can 1 (n.95) Glasses 1 (n.93) Bottles and can 2 (n.95) Milk (n.33) Yogurt (n.35) Cheese 2 (n.31) Cheese 1 (n.30) Mozzarella (n.34) Cottage cheese (n.32) Beef steak (n.40) Chicken breast (n.42) Pork steak (n.40) Cod fillet (n.54) Ham (n.46) Salami (n.48) Omelette (n.36) Pasta and tomato sauce (n.22) Bread (n.4) Crackers (n.1) Bread sticks (n.3) Boiled potatoes (n.28) Pizza (n. 8) Vegetable and greens (n. 61–71) Beans (n.26) Peas (n.29) Fresh fruit (n.72–80) Spoons, spoons and ladles (n.98) Spoons, spoons and ladles (n.98) Fat dressings (n.92) Fat dressings (n.92) Sweets (n.87, 88) French fries (n.27)
Espresso Red wine Lager beer Vodka Coke Semi-skimmed milk Whole milk yogurt Standard hard cheese serving Standard soft cheese serving Mozzarella Cottage cheese Semi-fat beef Chicken breast Pork Sirloin Chop Cod fillet Ham Salami Chicken eggs Semolina pasta Bread Crackers, bread sticks
Hamburger sandwich (n.82)
Hamburger sandwich Hot dog Panelle sandwich and spleen sandwich
Boiled potatoes Pizza tomato and mozzarella Lettuce, spinach, eggplant, zucchini, carrots Beans Peas Seasonal fresh fruit Extra-virgin olive oil Sunflower seed oil Butter Margarine Standard sweets serving French fries
Each item was recognized by the interviewee in the food atlas, and then a pre-specified corresponding referral food was considered for calculations in the MetaDieta software.
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Appendix C. Short-length FFQs
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ISSN: 0963-7486 (Print) 1465-3478 (Online) Journal homepage: http://www.tandfonline.com/loi/iijf20
Validation of a food frequency questionnaire for use in Italian adults living in Sicily Silvio Buscemi, Giuseppe Rosafio, Sonya Vasto, Fatima Maria Massenti, Giuseppe Grosso, Fabio Galvano, Nadia Rini, Anna Maria Barile, Vincenza Maniaci, Loretta Cosentino & Salvatore Verga To cite this article: Silvio Buscemi, Giuseppe Rosafio, Sonya Vasto, Fatima Maria Massenti, Giuseppe Grosso, Fabio Galvano, Nadia Rini, Anna Maria Barile, Vincenza Maniaci, Loretta Cosentino & Salvatore Verga (2015) Validation of a food frequency questionnaire for use in Italian adults living in Sicily, International Journal of Food Sciences and Nutrition, 66:4, 426-438 To link to this article: http://dx.doi.org/10.3109/09637486.2015.1025718
Published online: 01 Apr 2015.
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Date: 11 September 2015, At: 15:04
http://informahealthcare.com/ijf ISSN: 0963-7486 (print), 1465-3478 (electronic) Int J Food Sci Nutr, 2015; 66(4): 426–438 ! 2015 Informa UK Ltd. DOI: 10.3109/09637486.2015.1025718
STUDIES IN HUMANS
Validation of a food frequency questionnaire for use in Italian adults living in Sicily Silvio Buscemi1, Giuseppe Rosafio1, Sonya Vasto2, Fatima Maria Massenti3, Giuseppe Grosso4, Fabio Galvano4, Nadia Rini1, Anna Maria Barile1, Vincenza Maniaci1, Loretta Cosentino1, and Salvatore Verga1 Downloaded by [University of Nebraska, Lincoln] at 15:04 11 September 2015
1
Dipartimento Biomedico di Medicina Interna e Specialistica (DIBIMIS) – Laboratorio di Nutrizione Clinica, 2Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche (DIBIMEF) and 3Dipartimento di Scienze per la Promozione della Salute e Materno Infantile, University of Palermo, Palermo, Italy, and 4Dipartimento di Scienze del Farmaco, University of Catania, Catania, Italy
Abstract
Keywords
The objective of this study was to validate two interviewer-led food frequency questionnaires (FFQs) of very different lengths: a medium-length FFQ (medium-FFQ) of 36 items and a shortlength FFQ (short-FFQ) of 18 items, intending to measure levels of intakes in a local population. Both FFQs were validated against intakes derived from a 3-day dietary record (3-day DR). Sixtyfive non-diabetic adults with no known cardiovascular, renal or other systemic diseases were included. High correlation coefficients between the FFQ and the 3-day DR (0.45–0.73) were observed for energy intake, carbohydrates and lipid and protein intake. Bland–Altman plots showed good agreement between the methods. Low (0.26–0.37) correlation coefficients of the different nutrient intakes obtained with the short-FFQ and the 3-day DR were observed, with the exception of alcohol intake (rho ¼ 0.49). This study showed promising evidence for the use of a medium-FFQ as a potentially useful tool for investigating the relationship between habitual diet and diseases in clinical and research settings.
Dietary record, energy intake, food frequency questionnaire, food intake
Introduction Dietary habits are important determinants of lifestyle, and play a central role in the pathogenesis and treatment of a number of clinical conditions, particularly metabolic diseases such as diabetes (Knowler et al., 2002) and atherosclerosis (Stampfer et al., 2000). It is therefore particularly important that there be adequate tools for investigating the role of dietary habits, and for developing both prevention and intervention strategies in the special context of metabolic and cardiovascular diseases. Even on clinical grounds, investigating dietary habits is a very useful step toward targeting patients affected with diseases such as obesity, diabetes or atherosclerosis (Franz et al., 2004). No dietary method can measure dietary intake without error (Margetts & Nelson, 1997), so limits and the possibility of error need to be defined in order to avoid undue conclusions. Food frequency questionnaires (FFQs) are low-cost and easy-to-use tools (Block et al., 1990; Jacques et al., 1993). Traditionally, they have been used largely to describe habitual dietary intake, particularly in epidemiologic studies. Food consumption is culture dependent (Cassidy, 1994; Vasto et al., 2014), and therefore FFQs need to be adequately validated for the population of interest, as well as the degree to which the questionnaire measures items (foods or nutrients) for which it has been designed. Incorrect information may lead to false associations between dietary factors
History Received 27 October 2014 Revised 4 February 2015 Accepted 15 February 2015 Published online 1 April 2015
and diseases or disease-related markers. The Italian population was part of the Seven Country Study, which contributed significantly to describing the Mediterranean diet (Keys et al., 1986). However, dietary habits have changed profoundly (Sofi et al., 2005) since the time of that study, and developing current Italian regional FFQs may provide important information about the evolution of dietary habits in the last few decades. Various FFQs have been proposed, with substantial differences in terms of length (from 5 to 350 items) and consequent time required for their use (Cade et al., 2004). The length of a FFQ is critical in ensuring an appropriate balance between accuracy and time of use, thus allowing valid application in large samples of a population. Relatively few studies have been conducted to evaluate the validity of FFQs among Italians, and no study of residents of Sicily (one of the largest regions in Italy), where there is a high prevalence of diet-related diseases, such as obesity and diabetes (Buscemi et al., 2013). Therefore, we designed two interviewer-led FFQs of very different lengths: a medium-length (36 items) FFQ (medium-FFQ) and a short (18 items) FFQ (shortFFQ), intending to measure levels of intakes in the local population. The aim of this study was to validate both FFQs versus a 3-day dietary record (3-day DR), one of the most widely used validation methods in the literature (Cade et al., 2004).
Materials and methods Selection of participants Correspondence: Silvio Buscemi, Dipartimento Biomedico di Medicina Interna e Specialistica (DIBIMIS) – Laboratorio di Nutrizione Clinica, University of Palermo, Palermo, Italy. Tel/Fax: +39 0916554580. E-mail: [email protected]
Participants were recruited from the cohort drawn from the general population of a previous study held in Palermo that investigated the relationship between diet and cardiometabolic
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risk factors (Buscemi et al., 2013). Briefly, participants were invited to participate in the study through telephone contact or e-mail. People of both genders with age ranging from 18 to 80 years were included. Exclusion criteria were the presence of diabetes (type 1 or 2) on insulin treatment, clinically known atherosclerotic diseases (coronary heart disease, previous stroke, carotid or peripheral atherosclerosis), chronic renal failure and other systemic or organ failure diseases including neurodegenerative disorders, as determined by medical history, physical examination and routine blood tests. The study was conducted from January to December 2013. Participants were asked to answer a medium-FFQ designed according to local dietary habits. They were also requested to compile a 3-day DR within the following 2 weeks, during which they were also asked to maintain their usual lifestyle. When participants returned to our center with the compiled 3-day DR, a short-FFQ was also administered. Energy intake and macro- and micro-nutrients were calculated using the Metadieta software application (Me.Te.Da., San Benedetto del Tronto, Italy), which includes the Italian database of food composition (INRAN/IEO, 2008). Data were entered twice by two registered dieticians and then analyzed and carefully checked for errors by a third registered dietician. The dieticians were blinded to the study. The final report indicated the reported intakes of calories, fats (saturated, monounsaturated, unsaturated), carbohydrates, proteins, fiber and micro-nutrients. Data were collected quarterly, beginning in January 2013, using all three tools within the same month to minimize seasonal variations in eating patterns. At first visit, demographic and clinical information was collected. Height (m) and body weight (kg) were measured by a professional nurse; body mass index was calculated as body weight/height2 (kg/m2). The study was conducted according to the guidelines in the Declaration of Helsinki, and the study protocol was approved by the Investigator Revisory Board at the Biomedical Department of Internal and Specialist Medicine of the University of Palermo. Each participant approved and signed an informed consent form. Three-day dietary record Habitual energy intake was assessed using the 3-day DR method, as previously described (Buscemi et al., 1997). Briefly, it consisted of a three-page standardized recording form with written instructions: participants were instructed to record all food and beverages they had consumed for 3 consecutive days, including a Sunday, in a food diary. The 3-day DR included photographs of 15 foods, each with three different portion sizes. Participants could choose which photograph represented their portion size, and were also allowed to describe their portion size in other measures, such as weight or household units. The 3-day DR was used as the reference method. FFQ and short-FFQ Half-quantitative habitual intake of different foods during the previous 12 months was assessed with a newly designed mediumFFQ as presented in Appendix A. Specifically, participants were asked by trained dieticians about their consumption of 36 different food items based on commonly eaten foods and portion sizes of the food list relative to previous data collected through a 24-h dietary recall from 422 residents in the urban area of Palermo (Buscemi et al., 2011). Participants were also asked to indicate their usual rate of consumption, choosing from seven frequency categories, ranging from ‘‘never’’ or ‘‘less than once a week’’ to ‘‘seven times per week.’’ The food items were categorized as drinks, milk and dairy products, meat–fish–eggs, cereals, vegetables–legumes–fruit, fatty dressings and other (sweets, fried foods and fast food). For coffee, alcoholic and soft drinks, the
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amount in terms of cups/glasses was also specified. In most instances, a reference food was considered for different items, and photographs of food were used to help in portion-size description (Atlas Scotti Bassani, 2010), as indicated in Appendix B. Requested data referred to the last year. A short version of the above-described FFQ (short-FFQ) was also designed. It was interviewer-led and consisted of 18 items, as presented in Appendix C. Statistical analysis Participants with a missing or incomplete 3-day DR were excluded from evaluation. Participants with evidence of clear underreporting of food intake from the 3-day DR (their reported energy intake was lower than the predicted basal metabolic rate in the presence of a stable body weight) were also excluded. Since a different body weight between the two test days may indicate different energy intakes at the time of each test (FFQs versus 3-day DR), participants whose body weight from medium-FFQ measurement to the 3-day DR changed 4± 1.0 kg were also excluded. Normal distribution of the variables was tested using the Kolmogorov–Smirnov test. The mean and standard deviation for each nutrient obtained from the medium-FFQ, short-FFQ and 3-day DR were calculated. The validity of both the medium-FFQ and the short-FFQ was compared with the 3-day DR using Pearson correlation coefficients. Rank correlation with Spearman’s coefficient (rho) was calculated in the case of not normally distributed variables with observations of zero values. Between-method differences were analyzed with the Wilcoxon test. A p value of less than 0.05 was considered statistically significant. The Bland–Altman method was used to produce quantitative estimates of agreement between the methods (Bland & Altman, 1986). The Bland–Altman analysis was performed for energy, protein, fat and carbohydrate intake. The sample size of the analysis is within the range of previous studies in this field (Cade et al., 2002). All statistical analyses were performed using MedCalc Statistical Software version 13.3.3 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014).
Results One-hundred-eighteen volunteers gave their consent to participate in the investigation; 87 were eligible, and 22 were excluded due to inadequate 3-day DR based on previously established criteria. Therefore, 65 participants were considered for analysis. Demographic and clinical characteristics of participants are reported in Table 1. The nutritional variables were normally distributed except for alcohol, fiber, cholesterol, polyunsaturated fat and n3 and n6 fatty acid intakes obtained with all three methods used in the study. Correlation coefficients for the variables obtained using the FFQ and 3-day DR are reported in Table 1. Significant correlations were observed for all dietary variables with the exception of cholesterol, calciferol and potassium. Moderate to high correlation coefficients (0.45–0.73) with good agreement between the two methods were observed for energy intake (kcal/ day), carbohydrates (g/day), and lipid (g/day) and protein intake (g/day) (Figure 1). However, the medium-FFQ underestimated energy intake by an average of 10.6%, and carbohydrate intake by 35%, with minimal bias for protein and fat intakes. Correlation coefficients higher than 0.40 (0.48–0.65) were also observed for saturated, monounsaturated, polyunsaturated, n6 fat, calcium and phosphorus intakes, as well as for alcohol intake (rho ¼ 0.39). However, coupled with examination of the Bland–Altman plots (Figure 2), a lack of agreement between methods was observed for polyunsaturated fatty acid and n6 fatty acid intakes (Figure 2C and D). The Bland–Altman plots confirmed a good
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Table 1. Participant characteristics, daily energy and nutrient intakes according to 3-Day DR, and medium and short FFQ.
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Mean ± SD or prevalence Males/females, n (%) Age (years) Marital status Married, n (%) Single, n (%) Smoking habits Current smokers, n (%) Cigarette/day (n) Ex-smokers, n (%) Coffee consumption (cups/day) Body weight (kg) BMI (kg/m2) Waist circumference (cm) Waist-to-hip ratio Systolic BP (mmHg) Diastolic BP (mmHg) Heart rate (beats/min)
Energy intake (kcal/day) Carbohydrate (g/day) Protein (g/day) Fat (g/day) Saturated fat (g/day) Monounsaturated fatty acids (g/day) Polyunsaturated fatty acids (g/day)b n3 Fatty acids (g/day)b n6 Fatty acids (g/day)b Cholesterol (mg/day)b Alcohol (g/day)b Fiber (g/day)b Calciferol (mcg/day) Calcium (mg/day) Phosphorus (mg/day) Sodium (mg/day) Potassium (mg/day)
26 (40)/39 (60) 53 ± 15
Range 18–79
58 (89.2) 7 (10.8) 8 (12.3) 19 ± 10 23 (35.4) 2.5 ± 1.6 93.7 ± 25.1 35.0 ± 7.3 110 ± 17 0.96 ± 0.09 139 ± 18 85 ± 12 72 ± 10
0–6 44–164 21.8–60.3 72–156 0.73–1.09 100–185 65–115 50–91
3-Day DR
Medium-FFQ
1825 ± 464 234 ± 60 73 ± 20 70 ± 27 22 ± 10 37 ± 11 8±4 0.8 ± 0.4 6.2 ± 3.8 237 ± 117 2.6 ± 11.2 19 ± 6 2.6 ± 2.7 659 ± 258 1081 ± 275 1689 ± 832 2404 ± 638
(1044–3331) (116–441) (44–122) (15–175) (5–60) (10–77) (3–26) (0.3–2.5) (2.0–21.7) (52–573) (0–86) (10–40) (0.2–13.6) (218–1620) (486–1651) (116–4548) (1059–4382)
5–40
1618 ± 372 155 ± 44 72 ± 15 77 ± 26 24 ± 7 38 ± 15 8±4 0.9 ± 0.3 6.6 ± 3.3 280 ± 68 4 ± 10 13 ± 4 1.5 ± 0.5 766 ± 208 1132 ± 216 1057 ± 302 2247 ± 455
(733–2441) (72–330) (45–119) (29–160) (11–42) (13–93) (3–21) (0.4–1.6) (1.9–19) (115–480) (0–64) (7–28) (0.6–2.7) (347–1243) (691–1554) (452–1717) (1005–3300)
ra
p
0.73 0.56 0.45 0.68 0.48 0.65 0.48 0.31 0.49 0.23 0.93 0.32 0.11 0.48 0.54 0.29 0.18
50.001 50.001 50.001 50.001 50.001 50.001 50.001 0.01 50.001 0.07 50.001 0.01 0.46 50.001 50.001 0.02 0.17
Short-FFQ 959 ± 178 61 ± 16 51 ± 9 54 ± 10 18 ± 3 32 ± 5 4±1 0.5 ± 0.1 3.6 ± 0.6 183 ± 33 4 ± 10 5±1 1.0 ± 0.3 633 ± 154 805 ± 141 686 ± 172 1240 ± 289
(645–1636) (19–82) (32–67) (23–71) (7–23) (10–35) (0.4–0.7) (0.3–0.7) (1.8–4.7) (115–251) (0–64) (2–6) (0.5–1.9) (330–884) (512–1123) (342–1040) (580–2486)
ra
p
0.32 0.01 0.20 0.11 0.32 0.01 0.37 0.003 0.26 0.04 0.33 0.009 0.19 0.15 0.12 0.36 0.17 0.18 0.19 0.14 0.92 50.001 0.24 0.06 0.21 0.11 0.30 0.018 0.36 0.004 0.10 0.44 0.02 0.90
Values are means ± SD, range in parenthesis. a Pearson’s correlation coefficient (Spearman’s coefficient of rank correlation in the case of alcohol). b Not normally distributed variables, log-transformed for analysis and then back-transformed for presentation. BMI, body mass index; BP, blood pressure.
between-methods agreement for saturated and monounsaturated fat intakes (Figure 2A and B), calcium and phosphorus intakes (Figure 3) and alcohol consumption (Figure 4A). Low but significant r values (0.29–0.37) were found with regard to n3 fatty acids, fiber and sodium intakes. As a result, Bland–Altman analysis was not performed. Energy and seasonal adjustment did not improve the observed between-methods correlations for the above nutrients (data not shown). The correlation coefficients of the different nutrient intakes obtained with the short-FFQ and 3-day DR are reported in Table 1. Significant correlations were observed for energy intake and for the daily intakes of proteins, fats, saturated fats, monounsaturated fatty acids (MUFA), alcohol, calcium and phosphorus. However, the correlation coefficients were rather low (0.26–0.37) with the exception of alcohol intake (rho ¼ 0.49). The short-FFQ highly underestimated energy (47.5% on average), fat (19.4%) and protein intakes (30.1%). Given the low values of the correlation coefficients, the Bland–Altman analysis was not performed, with the exception of alcohol intake (Figure 4B), which confirmed a good between-methods agreement.
Discussion It is imperative that a FFQ be designed and validated for the population in whom it is used. Differences between populations
make it necessary to develop culturally sensitive tools for the evaluation of dietary habits. In this study, we attempted to validate two FFQs (a medium-length and a short-length FFQs) designed to assess current diet in Italian adults living in Sicily, a Mediterranean-diet-based population. Both FFQs were compared with a 3-day DR. The results confirmed the hypothesis that the medium-FFQ provides evaluations similar to the 3-day DR for many of the variables investigated. By contrast, the hypothesis was not true with regard to the evaluations obtained with the short-FFQ, as considerably large differences with values obtained with 3-day DR were observed. This was not the case for habitual alcohol consumption, thus confirming that short-length FFQs may be applicable only when single nutrient intakes have to be assessed (Cade et al., 2004; Montomoli et al., 2002). Our medium-FFQ provided a good evaluation of different variables, including energy intake and the intakes of lipids, proteins, carbohydrates, SFA, MUFA, calcium, phosphorus and alcohol. The agreement between the medium-FFQ and the 3-day DR was verified according to the recommendations of the consensus document on the development of a medium-FFQ (Cade et al., 2002), using correlation analysis in conjunction with the Bland– Altman method. Even with the medium-FFQ significant differences with respect to the 3-day DR estimates were observed. The medium-FFQ underestimated the energy and carbohydrate
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Figure 1. Bland–Altman plots for energy intake and macronutrients (A, energy; B, carbohydrate; C, fat; D, protein intakes) with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
intakes. Over/under estimation by FFQs is an expected error, and similarly to other studies (Jackson et al., 2011; Xia et al., 2011) we did not observe improvements in any correlation after energy adjustments (data not shown). It is worth considering that as in our study, validation studies of FFQs are generally (Cade et al., 2002) performed in comparison with dietary record methods that are far from being real gold standards. In this case, a gold standard in terms of ‘‘truth reference’’ is not available, and food records suffer from measurement errors that are likely correlated with errors in the medium-FFQ (Goris et al., 2000; Ocke & Kaaks, 1997). Given the absence of a real gold standard it would be more appropriate to refer to studies of relative validity than to validation studies. Interestingly, we also found that mean calcium intake estimated according to the 3-day DR (659 mg/day) or the medium-FFQ (766 mg/day) was almost similar to the mean value (820 mg) of the Italian population as reported by a wider research study (SINU, 1996). This difference could likely be rectified by estimating the intake of calcium in water (Montomoli et al., 2002), a variable that was not considered in our study. Furthermore, a more recent Italian survey (Sette et al., 2011) indicated an average habitual calcium intake of 767 mg/day, a value that is more similar to the estimation we obtained. In their review, Cade et al. (2004) observed that the method of administration of an FFQ played a critical role in validation studies, and correlation coefficients between the FFQ and reference measures were generally higher for interviewer-administered than for self-administered questionnaires. The good level of accuracy found in our medium-FFQ, despite its limited number of items, may be due, at least in part, to the fact that it was
administered by registered dieticians and not self-administered. Furthermore, our medium-FFQ was newly designed, and in the same review by Cade et al. it was reported that correlation coefficients were higher for newly designed FFQ than for those adapted from other questionnaires. With the exception of the FFQ designed for the European Prospective Investigation into Cancer and Nutrition (EPIC) study (Decarli et al., 1996; Faggiano et al., 1992; Pala et al., 2003; Pisani et al., 1997), the aim of which was to investigate the associations between diet and cancer, no FFQ has been designed to assess the current diet of people living in South Italy. Italian FFQs have been developed to assess the habitual intake of specific micro-nutrients (Cena et al., 2008; Gonnelli et al., 2009; Montomoli et al., 2002; Pellegrini et al., 2007; Porrini et al., 1995; Turconi et al., 2010), the diet risk associated with cancers (Cade et al., 2004), disease-specific diet associations (Cade et al., 2004), current diets in northern Italy (Fidanza et al., 1995) and, finally, in children/adolescents (Bellu et al., 1996; Bertoli et al., 2005; Pampaloni et al., 2013). The medium-FFQ that we validated might be a useful tool for investigating the associations between diet, metabolic and cardiovascular diseases in this population at high risk for obesity and diabetes. A number of FFQs have been proposed, with the number of items ranging from 5 to 350, thus varying from very easy, but imprecise, questionnaires (as was our short-FFQ) to very accurate questionnaires, which are, however, time-consuming and stressful for the participant (Cade et al., 2004). Very detailed and long FFQs may lead to potential biases when administered to people
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Figure 2. Bland–Altman plots for dietary contents of (A) saturated fatty acids (SFA); (B) monounsaturated fatty acids (MUFA); (C) polyunsaturated fatty acids (PUFA) and (D) n6 fatty acids (n6 FA) with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
Figure 3. Bland–Altman plots for dietary contents of (A) calcium and (B) phosphorus with mean difference (solid line) between medium-length FFQ and 3-day DR and 95% limits of agreements (dashed lines).
with low levels of education, especially when self-administered. Our medium-FFQ takes about 10–15 min to be administered, and may have the merit of conjugating optimal length and accuracy. Our study has several limitations, one of which is the rather limited number of participants, although the number of participants in our study was in the recommended range (50–100) for
this kind of study (Cade et al., 2002), and it has been noted (Cade et al., 2004) that the size of the validation study does not make an appreciable difference to the study results. The main limitation of our study may be that we did not specifically address the issue of repeatability, however, it has been affirmed (Altman, 1995) that a method with poor repeatability hardly agrees well
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Figure 4. Bland–Altman plots for alcohol intake with mean difference (solid line) between medium-length (A), short-length (B) FFQ and 3-day DR and 95% limits of agreements (dashed lines).
with another method, and that good agreement is most unlikely unless the two methods are both accurate and repeatable. Although we did not measure the interviewer inter- and intravariability, another limitation, the dieticians were adequately trained, and we believe that in the case of significant interviewer inter- and intra-variability our results would have been weaker. In conclusion, our study shows that a medium-FFQ is not difficult to design for specific populations, and likely improves the accuracy of studies that aim to investigate the relationship between habitual diet and diseases.
Delcaration of interest This study was funded by Associazione Onlus Nutrizione e Salute, Palermo, Italy.
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Appendix A. Medium-length FFQs
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Appendix B. Procedure used to calculate energy and nutrients content from FFQ
Categories
Items
Drinks
Coffee Wine Beer Strong drinks Soft drinks Milk Yogurt Hard cheese Soft cheese Mozzarella Cottage cheese Red meat Poultry Pork Fish Cured meat Salami Eggs Pasta/rice Bread Bakery products
Milk and dairy products
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Meat, fish and eggs
Cereals
Vegetables, legumes and fruits
Fatty dressings
Other
Potatoes Pizza Vegetables/greens Dried legumes Fresh legumes Fresh Fruits Olive oil Seed oil Butter Margarine Sweets Fried foods Fast food Hamburger Hot dog Local street food
Scotti-Bassani Atlas reference (table number)
MetaDieta software selection
Cup (n.99) Glasses 2 (n.94) Bottles and can 1 (n.95) Glasses 1 (n.93) Bottles and can 2 (n.95) Milk (n.33) Yogurt (n.35) Cheese 2 (n.31) Cheese 1 (n.30) Mozzarella (n.34) Cottage cheese (n.32) Beef steak (n.40) Chicken breast (n.42) Pork steak (n.40) Cod fillet (n.54) Ham (n.46) Salami (n.48) Omelette (n.36) Pasta and tomato sauce (n.22) Bread (n.4) Crackers (n.1) Bread sticks (n.3) Boiled potatoes (n.28) Pizza (n. 8) Vegetable and greens (n. 61–71) Beans (n.26) Peas (n.29) Fresh fruit (n.72–80) Spoons, spoons and ladles (n.98) Spoons, spoons and ladles (n.98) Fat dressings (n.92) Fat dressings (n.92) Sweets (n.87, 88) French fries (n.27)
Espresso Red wine Lager beer Vodka Coke Semi-skimmed milk Whole milk yogurt Standard hard cheese serving Standard soft cheese serving Mozzarella Cottage cheese Semi-fat beef Chicken breast Pork Sirloin Chop Cod fillet Ham Salami Chicken eggs Semolina pasta Bread Crackers, bread sticks
Hamburger sandwich (n.82)
Hamburger sandwich Hot dog Panelle sandwich and spleen sandwich
Boiled potatoes Pizza tomato and mozzarella Lettuce, spinach, eggplant, zucchini, carrots Beans Peas Seasonal fresh fruit Extra-virgin olive oil Sunflower seed oil Butter Margarine Standard sweets serving French fries
Each item was recognized by the interviewee in the food atlas, and then a pre-specified corresponding referral food was considered for calculations in the MetaDieta software.
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Appendix C. Short-length FFQs
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